{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Minimal example for working with NGIMU data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the worklab module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import worklab as wl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the data with `com.load()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "Initializing loading for C:\\Users\\rick_\\Development\\worklab2\\example_data\\imu_example_data\\Session.xml ...\n",
      "Folder identified as NGIMU folder. Attempting to load ...\n",
      "Data loaded!\n",
      "================================================================================\n",
      "\n",
      "NGIMU data is stored in a:  <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "filename = os.getcwd()\n",
    "filename = os.path.join(os.path.split(filename)[0], 'example_data', 'imu_example_data', 'Session.xml')\n",
    "imu_data = wl.com.load(filename)\n",
    "print(\"NGIMU data is stored in a: \", type(imu_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The structure is as follows: you have a dictionary with all devices, that contains a dictionary with all sensors. Sensordata is stored in Pandas DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imu_data contains:  dict_keys(['Frame', 'Right', 'Left'])\n",
      "Frame contains:  dict_keys(['sensors'])\n",
      "sensors contains:  Index(['Time', 'GyroscopeX', 'GyroscopeY', 'GyroscopeZ', 'AccelerometerX',\n",
      "       'AccelerometerY', 'AccelerometerZ', 'MagnetometerX', 'MagnetometerY',\n",
      "       'MagnetometerZ', 'Barometer'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"imu_data contains: \", imu_data.keys())  # dict\n",
    "print(\"Frame contains: \", imu_data[\"Frame\"].keys())  # dict\n",
    "print(\"sensors contains: \", imu_data[\"Frame\"][\"sensors\"].columns)  # DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can resample the IMUs to a fixed frequency with `move.resample_imu()` which takes a session data object and a sample frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Freq before resampling:  397.0700650101465\n",
      "Freq after resampling:  400.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Freq before resampling: \", 1 / imu_data[\"Frame\"][\"sensors\"][\"Time\"].diff().mean())\n",
    "imu_data = wl.move.resample_imu(imu_data, sfreq=400)\n",
    "print(\"Freq after resampling: \", 1 / imu_data[\"Frame\"][\"sensors\"][\"Time\"].diff().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have the IMUs attached to a wheelchair you can use `move.calc_wheelspeed()` to get wheelchair performance related variables (the function needs some wheelchair specific information):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "imu_data = wl.move.calc_wheelspeed(imu_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you use the data structure for wheelchair related variables the different sensor keys get dropped and just the relevant DataFrames are kept.\n",
    "You can visualize the data using Pandas built-in plot function or with matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imu_data[\"Frame\"].plot(\"Time\", \"CombSkidVel\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There you go! You can consider some filtering now and extracting the variables you need."
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}